Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
Querying local SQLite index...
g-skl-platform-warp
by wrm3Authoritative reference for Warp (AI terminal + Oz agent platform) customization in gald3r projects. Covers AGENTS.md/WARP.md project rules, Agent Skills (SKILL.md) cross-vendor discovery, Agent Profiles + Oz subagent orchestration, Warp Drive Workflows + MCP, the absent hook surface, and gald3r install verification.
g-skl-platform-antigravity
by wrm3Authoritative reference for Google Antigravity (agent-first IDE + CLI + SDK) customization in gald3r projects. Covers AGENTS.md/GEMINI.md instruction files, .agents/ rules/skills/hooks + .antigravity/mcp.json, Workflows (slash commands), dynamic subagents, lifecycle hooks, Scheduled Tasks, and gald3r install verification. Post-2.0-relaunch (I/O 2026); skill path + hook payload pin via install test.
your-skill-name
by wrm3Brief description of what this skill does and when to use it
g-skl-platform-openhands
by wrm3Authoritative reference for OpenHands (All Hands AI, formerly OpenDevin) AI agent customization in gald3r projects. Covers AGENTS.md context, .agents/skills + .agents/agents File-Based Agents, .openhands/hooks.json lifecycle hooks, config.toml MCP, Plugins bundle, and gald3r install verification.
g-skl-platform-cline
by wrm3Authoritative reference for Cline (VS Code / JetBrains extension + CLI + SDK) customization in gald3r projects. Covers .clinerules rules + workflows-as-slash-commands, .cline/skills SKILL.md, lifecycle hooks (macOS/Linux only), SDK subagents/teams, MCP marketplace, AGENTS.md (not CLAUDE.md), and gald3r install verification.
skl-seedance
by wrm3Seedance 2.0 AI video generation — text-to-video and image-to-video via VolcEngine, fal.ai, or Replicate. Async submit → poll → download → vault note. No Docker, no MCP. Stores in research/videos/.
trellis2-gguf
by wrm3Trellis 2 GGUF ΓÇö local image-to-3D pipeline in ComfyUI on 6GB+ VRAM. Generates full 3D meshes with textures + UV unwrap from a single image. Use for characters, props, hard-surface assets, and architectural exteriors. NOT for room interiors, text-to-3D, or scene composition.
g-skl-platform-monitor
by wrm3Cross-platform health and freshness monitor for the 23 gald3r platforms. Checks per-platform capability gaps against the Cursor reference, scans official docs for breaking changes, validates platform-specific config, and generates the PLATFORM_STATUS / PLATFORM_CAPABILITY_MATRIX living indexes. Owned by g-agnt-platformer.
skl-higgsfield
by wrm3Higgsfield AI image-to-video (DoP cinematic motion) via the Higgsfield public API. Async submit → poll → download → vault note. No Docker, no MCP. Pure httpx. Stores in research/videos/.
3d-performance
by wrm3Performance optimization for 3D web scenes ΓÇö LOD strategies, frustum/occlusion culling, draw call reduction, and R3F-specific optimizations. Use when scenes run below 60fps.
storyboard-creation
by wrm3StoryboardsΓÇöshots, angles, movement, continuity, pre-production for film and animation.
atlassian-integration
by wrm3Complete integration with Atlassian products (Jira, Confluence, Bitbucket, Trello) for project management and documentation
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.